SERVICE DECISION INTELLIGENCE / AUTONOMOUS SERVICE

Autonomous Repair Journey

From customer concern to completed repair

A coordinated journey of specialized Service AI Workers that assist people and automate repeatable work across intake, diagnosis, field reporting, repair execution, warranty decisions, and repair completion.

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One shared Repair Context continuously enriched
01Customer
Concern
02Guided
Intake
03Technician
Diagnosis
04Field Report
& Evidence
05Repair Plan
& Execution
06Warranty
/ Claim
07Repair
Completion
◎Better CaptureComplete, consistent customer and technician data
↗Faster WorkLess re-keying, searching, and report assembly
◇Better DecisionsGuided diagnosis, repair, parts, and warranty decisions
⌁Earlier InsightHigher-quality field data for emerging issue detection
WHY THE REPAIR JOURNEY NEEDS TO CHANGE

Too much service knowledge is lost between the conversation and the completed repair.

Critical service information is fragmented across people, conversations, forms, and systems. Every handoff creates another opportunity to lose context, introduce inconsistency, or delay resolution.

01

Customer voice gets compressed

Symptoms may be summarized too early or captured without the operating conditions required for diagnosis.

02

The right questions vary by person

Experience, workload, language, and product familiarity change the quality of intake and follow-up.

03

Technician knowledge is captured late

Tests, measurements, observations, and reasoning are often documented after the work instead of during it.

04

Field reports become admin work

Teams reconstruct narratives and assemble evidence after the service activity has already happened.

05

Systems hold different versions

The repair order, field report, parts record, and warranty claim can tell slightly different stories.

06

Manufacturers see patterns late

Inconsistent symptom, cause, and repair data slows field-quality analysis and early issue detection.

THE OPPORTUNITY

Capture the service event once, at the moment the information is created. Then progressively enrich the same governed repair context through diagnosis, evidence, repair, warranty, and closure—so downstream records are generated from the work, not reconstructed afterward.

THE AUTONOMOUS REPAIR JOURNEY

One service event. One continuously enriched context.

Specialized AI Workers assist where expertise matters and automate repeatable work where confidence, evidence, and business controls allow.

01◉Customer ConcernPreserve the original customer voice.
02✦Guided IntakeAsk the next best question.
03⌁Technician DiagnosisGuide and capture while work happens.
04▤Field Report & EvidenceBuild the technical record automatically.
05⚙Repair Plan & ExecutionRecommend repair steps and parts.
06✓Warranty / ClaimApply coverage, policy, and evidence.
07↺Repair CompletionClose the loop and feed quality insight.
Customer voice→Operating conditions→Diagnostic findings→Evidence→Causal component→Repair action→Warranty decision→Repair outcome
01AssistPrompt, recommend, summarize
→
02ValidateCheck completeness and consistency
→
03DecideApply knowledge, policy, and decision models
→
04ActPopulate systems and execute approved steps
SERVICE AI WORKERS BY STAGE

Focused AI Workers collaborate across the repair lifecycle.

Each worker has a defined responsibility. Orchestration passes context forward, adapts questions and actions to the current repair state, and routes exceptions with evidence already assembled.

01–02Customer Concern + Guided IntakeService Advisor + Intake Agent · Assist / automate+

Listen in the customer’s preferred language, capture the original customer voice, identify the asset and reason for visit, and ask context-specific questions based on product, symptom, history, and missing information.

Multilingual voiceDynamic questionsOperating conditionsKnown asset data
03Technician DiagnosisService Advisor · Assist+

Guide the diagnostic sequence, surface approved knowledge, and capture tests, measurements, fault codes, observations, images, and diagnostic conclusions while the technician works.

Hands-free captureDiagnostic guidanceEvidence associationFact vs. conclusion traceability
04Field Report + EvidenceField Report Intelligence · Automate with review+

Generate a structured FSR/FTIR-style service record from the repair context, organize evidence, score completeness, identify contradictions, and request missing information before submission.

FSR / FTIRCompleteness scoringImage/video evidenceReview before submission
05Repair Plan + ExecutionService Advisor + Parts Advisor · Assist / automate+

Recommend approved repair procedures and appropriate components, capture work performed and deviations, and keep the digital repair record synchronized with physical execution.

Repair proceduresParts recommendationsDeviation captureRO synchronization
06Warranty / Claim DecisionWarranty Advisor + Claim Agent · Assist / automate+

Evaluate coverage, policy, labor, parts, repair context, and evidence. Prepare or update the claim using information already captured upstream and route exceptions for human review.

CoveragePolicyParts & laborException routing
07Repair CompletionService Advisor + Quality Analyst · Assist / automate+

Generate a clear customer-facing repair summary, verify closure data, capture outcomes, identify repeat-repair signals, and feed structured service data into quality and field analytics.

Customer summaryClosure checksOutcome captureField quality feed
GENERIC AI-ASSISTED SERVICE REPORT

Generate the technical record from work already performed.

Conversation, diagnostic activity, system data, and evidence are transformed into a structured service record that can map to an existing DMS, FSM, ERP, warranty platform, or dealer system.

  • Preserve customer voice instead of reconstructing it later.
  • Link diagnostic actions and evidence to the relevant symptom.
  • Guide and label images, video, measurements, and causal components.
  • Create structured fields and narrative from the same source data.
  • Reuse the completed record for the repair order, warranty claim, and field-quality analysis.
AI-ASSISTED SERVICE REPORTService Case SR-000184
Complete
Asset / SerialModel X / SN••••184Report QualityMissing items: none
Customer Voice

Intermittent vibration above highway speed; started after recent service.

Operating Conditions

65–75 mph · warm · light load

Observed Symptoms

Vibration reproduced · no warning lamp

Diagnostics

Road test · wheel inspection · scan · measurement

Cause

Pending technician confirmation

Corrective Action

Recommended steps from approved knowledge

Parts / Labor

Suggested and validated before posting

3 photos1 video✓ diagnostics✓ warranty context
DOWNSTREAM OUTPUTRepair Order + Warranty Claim + Field Quality Data
BETTER DATA → EARLIER FIELD INSIGHT

Every repair can make the next repair better.

Consistent customer symptoms, operating conditions, diagnostic findings, causal components, evidence, and repair outcomes give quality and engineering teams a more sensitive view of what is happening in the field.

◎

Cluster similar symptoms

Recognize related cases even when customers describe the same issue differently.

↗

Detect frequency shifts

See increases in a symptom, code, causal component, or repair combination earlier.

⌁

Compare field patterns

Analyze by model, configuration, production period, geography, operating condition, or service history.

↺

Close the learning loop

Feed confirmed field learnings back into future intake questions, diagnostic guidance, and repair recommendations.

BUSINESS CASE

Value for the service provider and the manufacturer.

The same journey can improve frontline productivity and customer experience while strengthening network consistency, warranty economics, and field-quality intelligence.

Business dimensionService provider / dealerManufacturer / OEM
01 Productivity

Less form filling, transcription, searching, and duplicate entry; faster handoffs.

Fewer clarification cycles and more consistent service records across the network.

02 Repair outcomes

Better intake and guided diagnosis improve the chance of the correct repair path.

Higher-quality field repairs, fewer repeat visits, and more consistent technical decisions.

03 Customer experience

More attention on the customer, multilingual interaction, and clearer repair updates.

A more consistent brand experience across dealers and service partners.

04 Warranty economics

Cleaner evidence and repair/claim linkage reduce rework and claim preparation effort.

More complete evidence, stronger policy consistency, and better visibility into cost drivers.

05 Field quality

Technicians capture richer facts and evidence as part of normal work.

Earlier detection of emerging issues and stronger engineering feedback.

06 Knowledge retention

Guidance helps less-experienced personnel and captures expert reasoning in the record.

Less dependence on tribal knowledge and a reusable service knowledge base.

PProductivityAdvisor / technician admin time · report preparation time
OOutcomesFirst-time fix · repeat repair · diagnostic cycle time
QQualityReport completeness · missing evidence · data consistency
$EconomicsClaim touch time · exceptions · warranty cost drivers
WHAT IS INCLUDED

A modular decision layer that works with the systems you already use.

The Autonomous Repair Journey does not require a new system of record. It coordinates people, AI Workers, approved knowledge, decision models, and enterprise systems around the repair.

01

Conversational capture

Voice and text for customer, advisor, technician, dealer, and service-provider input.

02

Multilingual support

Natural interaction across supported languages with normalized service terminology.

03

Guided questioning

Dynamic questions based on symptom, product context, policies, and missing information.

04

Service knowledge

Approved repair, diagnostic, and product knowledge with source-aware guidance.

05

Diagnostic assistance

Guided troubleshooting, test capture, evidence capture, confidence, and exception handling.

06

Structured documentation

FSR/FTIR-style reports, repair-order content, and claim-ready information.

07

Decision intelligence

Warranty, policy, repair, and evidence checks using governed rules and decision models.

08

Journey orchestration

State management and handoffs across AI Workers, people, and enterprise systems.

09

Analytics + feedback

Data-quality scoring, field issue analysis, outcome measurement, and continuous improvement.

TYPICAL INTEGRATIONS
DMS / FSMCRMERPWarranty / ClaimsParts SystemsService KnowledgeDiagnostic SystemsTelematicsImage / VideoIdentity & Access
GOVERNED AUTONOMY

Move from assistance to automation based on evidence, confidence, and control.

Human review remains available for high-risk, low-confidence, and exception decisions. Every recommendation, edit, source, and automated action can remain traceable.

Governance principles

  • Human review for high-risk, low-confidence, or exception decisions.
  • Role-based access and separation of user, system, and AI-generated information.
  • Approved service knowledge, policies, and decision models.
  • Traceability of sources, recommendations, edits, and automated actions.
  • Configurable completeness, confidence, and escalation thresholds.
  • Customer data segregation and integration into the existing system landscape.
PHASE 1CaptureVoice-assisted intake · multilingual questioning · structured service notes
PHASE 2DiagnoseTechnician guidance · diagnostic/evidence capture · field report generation
PHASE 3DecideRepair · parts · warranty decision support · confidence-based review
PHASE 4AutomateApproved downstream transactions · exception routing
PHASE 5LearnField issue analytics · closed-loop improvement
START PRACTICALLY

Choose one defined repair journey, a representative set of historical service cases, and the highest-friction handoffs. Baseline capture quality, report preparation time, and downstream rework—then prove guided capture and structured reporting before expanding autonomy.

FROM CUSTOMER CONCERN TO COMPLETED REPAIR

Make the repair journey easier for people, and more valuable to the manufacturer.

Capture better information, make better service decisions, and automate the administrative work around those decisions with specialized Service AI Workers.

Schedule a personalized demo See how Service AI Workers can fit into your existing service systems and repair process.
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